Abstract

There has been growing interest in the complex topology of human brain functional networks, often measured using resting state functional MRI (fMRI). Here, we used a meta-analysis of the large primary literature that employed fMRI or PET to measure task-related activation (>1600 studies; 1985-2010). We estimated the similarity (Jaccard index) of the activation patterns across experimental tasks between each pair of 638 brain regions. This continuous co-activation matrix was used to build a weighted graph to characterize network topology. We found that the co-activation network was modular, with occipital, central and default mode modules predominantly co-activated by specific cognitive domains (perception, action and emotion, respectively). It also included a rich-club of hub nodes, located in parietal and prefrontal cortex and often connected over long distances, which were co-activated by a diverse range of experimental tasks. Investigating the topological role of edges between a deactivated and an activated node, we found that such competitive interactions were most frequent between nodes in different modules or between an activated rich club node and a deactivated peripheral node. Many aspects of the co-activation network were convergent with a connectivity network derived from resting state fMRI data (N=27, healthy volunteers); although the connectivity network was more parsimoniously and less centrally connected. We conclude that the community structure of human brain networks was related to cognitive function. Deactivations may play a role in flexible reconfiguration of the network according to cognitive demand, varying the integration between modules, and between the periphery and a central rich club.